Incorporating pressure–volume traits into the leaf economics spectrum

Ecology Letters. 2023;00:1–14. wileyonlinelibrary.com/journal/ele | 1

L E T T E R

Incorporating pressure– volume traits into the leaf economics spectrum

Miquel Nadal

| María J. Clemente- Moreno

| Alicia V. Perera- Castro

| Margalida Roig- Oliver

| Yusuke Onoda

| Javier Gulías

| Jaume Flexas

1Departamento de Sistemas Agrícolas, Forestales y Medio Ambiente, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Zaragoza, Spain

2Research Group on Plant Biology under Mediterranean Conditions, Institut d'Investigacions Agroambientals i d'Economia de l'Aigua (INAGEA) – Universitat de les Illes Balears (UIB), Palma, Spain

3Department of Botany, Ecology and Plant Physiology, Universidad de La Laguna (ULL), La Laguna, Spain

4Graduate School of Agriculture, Kyoto University, Kyoto, Japan

Received: 19 July 2022

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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

© 2023 The Authors. Ecology Letters published by John Wiley & Sons Ltd.

Correspondence

Miquel Nadal, Departamento de Sistemas Agrícolas, Forestales y Medio Ambiente, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Avda.

Montañana 930, Zaragoza 50059, Spain.

Email: [email protected] Funding information

European Regional Development Fund, Grant/Award Number: PGC2018- 093824- B- C41; European Social Fund, Grant/

Award Number: BES- 2015- 072578; Japan Society for the Promotion of Science, Grant/Award Number: KAKENHI

#21H02564; Ministerio de Ciencia e Innovación, Grant/Award Number:

FJC2020- 043902- I; Ministerio de Ciencia, Innovación y Universidades, Grant/

Award Number: PGC2018- 093824- B- C41;

Ministerio de Economía y Competitividad, Grant/Award Number: BES- 2015- 072578 and FPU16/01544; Ministerio de Educación, Cultura y Deporte, Grant/

Award Number: FPU- 02054

Editor: Josep Penuelas

Abstract

In recent years, attempts have been made in linking pressure– volume parameters and the leaf economics spectrum to expand our knowledge of the interrelationships among leaf traits. We provide theoretical and empirical evidence for the coordination of the turgor loss point and associated traits with net CO₂ assimilation (A_n) and leaf mass per area (LMA). We measured gas exchange, pressure– volume curves and leaf structure in 45 ferns and angiosperms, and explored the anatomical and chemical basis of the key traits. We propose that the coordination observed between mass- based A_n, capacitance and the turgor loss point (π_tlp) emerges from their shared link with leaf density (one of the components of LMA) and, specially, leaf saturated water content (LSWC), which in turn relates to cell size and nitrogen and carbon content. Thus, considering the components of LMA and LSWC in ecophysiological studies can provide a broader perspective on leaf structure and function.

K E Y W O R D S

capacitance, leaf economics spectrum, leaf structure, leaf water content, photosynthesis, pressure–

volume, turgor loss point

I N TRODUCTION

The leaf economics spectrum (LES) provides a gen- eral framework for carbon economics and nutri- ent use in leaves among all plant groups and biomes (Wright et al., 2004), being net CO₂ assimilation (A_n), dark respiration rate (R_d), leaf mass per area (LMA), nutrient content (N and P) and leaf lifespan (LL) the central traits constituting a common axis of variation.

The coordinated shifts of these traits drive carbon

and nutrient use- efficiency in leaves, and they are in- tegrated into a fast versus slow continuum of strate- gies and processes at the plant and community levels (Cornwell et al., 2008; Díaz et al., 2016; Reich, 2014;

Westoby & Wright, 2006). Since the LES was estab- lished, an increasing number of studies have explored its coordination with other aspects of leaf physiology, in- cluding hydraulics and venation architecture (Blonder et al., 2011; Sack et al., 2013), mechanical properties (He et al., 2019; Onoda et al., 2011), anatomy, composition,

and nitrogen allocation (Hikosaka & Shigeno, 2009;

John et al., 2017; Onoda et al., 2017), and CO₂ diffusion inside leaves (Flexas et al., 2008; Niinemets et al., 2009).

These efforts have contributed to disentangle the in- terrelationships among leaf traits and to establish the mechanistic basis of the LES (Flexas et al., 2013; Onoda et al., 2017; Shipley et al., 2006). In addition, the avail- ability of extensive databases, being TRY the primary example (Kattge et al., 2011, 2020), has made possible for ecologists to explore the variability and responses of leaf traits at a much larger scale (e.g. Atkin et al., 2015;

He et al., 2020; Xing et al., 2021). However, there is still a need to integrate additional aspects of leaf physiology into this growing network of traits, and to do so with precise understanding of the exact nature of their asso- ciation. In particular, the inclusion of traits reflecting water stress tolerance would greatly help in discerning critical trade- offs at the leaf level (Reich, 2014), and in integrating the LES into upscaling models of veg- etation in a changing environment (Díaz et al., 2016;

van Bodegom et al., 2014). In this sense, here we aim to explore the coordination of photosynthesis and structure with pressure– volume (PV) traits, which re- flect plant strategies in relation to water availability (Bartlett, Scoffoni, et al., 2012; Xiong & Nadal, 2020;

Zhu et al., 2018).

PV traits describe water relations in leaves, that is, the interplay between water potential (pressure and os- motic potential) and water content and (symplast) vol- ume, averaged across all tissues and cells within the leaf (Tyree & Jarvis, 1982). Among the PV traits (listed in Table 1), the osmotic potential at the turgor loss point (π_tlp) is considered to be a key indicator of drought tol- erance. Lower π_tlp occurs in plants from drier habitats (Bartlett, Scoffoni, et al., 2012; Lenz et al., 2006; Zhu et al., 2018), and correlates with other indices of water stress tolerance, including minimum water potential experienced in the field (Zhu et al., 2018), stomatal and hydraulic decline (Bartlett et al., 2016; Martin- StPaul et al., 2017), and recovery after dehydration (John et al., 2018). π_tlp is strongly related to the osmotic potential at full turgor (π_o) and, to a lesser extent, the bulk modulus of elasticity (ε; Bartlett, Scoffoni, et al., 2012). Elasticity influences the relative water con- tent at the turgor loss point (RWC_tlp; Bartlett, Scoffoni, et al., 2012; Perera- Castro et al., 2020) and is usually associated with sclerophylly (Read et al., 2006; Salleo

& Nardini, 2000). The tissue components and/or cell arrangements that may determine ε are still unclear, al- though tissue density and cell wall thickness have been proposed to influence ε (Niinemets, 2001; Peguero- Pina, Sancho- Knapik, et al., 2017). Leaf capacitance, defined as the change in water content for a given range of water potential, has received less attention despite its potential role as a buffer protecting against rapid changes in water potential, especially in leaves with high transpiration capacity (Sack & Tyree, 2005; Xiong

& Nadal, 2020). Capacitance is also associated with the kinetics of stomatal closure and with dehydration tol- erance (see Xiong & Nadal, 2020). Recently, some stud- ies have explored the potential coordination between these parameters and the LES traits. Most notably, the extensive meta- analysis performed by Zhu et al. (2018) showed a negative correlation between π_tlp and LMA and, to a lesser extent, mass- based A_n (A_n,mass), thus providing a first hint to the potential coordination of carbon and water economics. However, the relationship between LMA and π_tlp reported by Zhu et al. (2018) is relatively weak and is elusive when considering specific plant groups and/or ecosystems (Májekova et al., 2021;

Maréchaux et al., 2020). On the other hand, Nadal et al.  (2018) described the relationship of area- based A_n (A_n,area) and ε, although it is not clear if this rela- tionship emerges through a common mechanistic basis, such as mesophyll cell wall thickness (Peguero- Pina, Sancho- Knapik, et al., 2017), or due to the necessity of increased capacitance in leaves subjected to high tran- spiration (Blackman & Brodribb, 2011; Sack et al., 2003;

Xiong & Nadal, 2020). These studies also highlight the need to consider area- and mass- based expressions of parameters, not only due to their distinct ecophysio- logical significance (Onoda & Wright, 2018; Westoby et al., 2013), but also due to the mathematical pitfalls associated with comparing non- independent variables (Lloyd et al., 2013; Osnas et al., 2013).

In the present study, we aim to provide theoretical and empirical evidence for the coordination between photosynthesis, structure and PV traits. First, a set of relatively simple equations provides the theoreti- cal framework for the analysis and interpretation of the leaf traits included in the present study (Table 1, Box 1). Our approach is based on the distinct consid- eration of area- and mass- based traits, and the separa- tion of LMA into its two components, leaf thickness (LT) and density (LD). This allows relating LD to the water content of leaves (Pyankov et al., 1999; Vile et al., 2005) and thus π_tlp and capacitance following Bartlett, Scoffoni, and Sack  (2012). Considering LT and LD independently is not a novel approach; in- deed, a number of studies have linked either thickness or density to a given variable to avoid the potential confounding effects of LMA (Hodgson et al., 2011;

Kitajima & Poorter, 2010; Niinemets, 1999; Witkowski

& Lamont, 1991). Here we hypothesise that the water content of leaves and LD (not LT) play a central role in leaf trait relationships through their effects on A_n,mass, π_o and leaf mass- specific capacitance at full turgor (C*_ft,mass). To test this hypothesis, we performed gas exchange, leaf structure and PV measurements in 45 ferns and angiosperms to explore the relationships described in Box 1. In addition, we also analysed leaf anatomy, carbon and nitrogen content and cell wall composition in the subset of the species from Nadal et al.  (2018) to further explore the anatomical and

chemical basis of the proposed relationships, given the key role of cell walls and N content in driving photosynthetic capacity (Flexas et al., 2021; Onoda et al., 2017). Cell wall composition (pectin, hemicel- lulose and cellulose content) was also included in the analysis due to recent work highlighting its association to both photosynthesis and PV traits in response to water stress (Roig- Oliver et al., 2020, 2021).

M ATER I A LS A N D M ET HODS Data collection

In the present study, we use a combination of previously published data and newly measured plants from the UIB campus (Palma de Mallorca, Spain). The list of species is displayed in Table S1. In some cases, plants growing

TA B L E 1 Main photosynthetic, structural and pressure– volume traits included in the study. See Box 1 for their derivation and the proposed theoretical framework describing their coordination.

Parameter Definition Units Significance Reference

Photosynthesis

A_n,mass Net CO₂ assimilation rate per dry mass

nmol g⁻¹ s⁻¹ Photosynthetic ‘return’ per unit of dry mass

‘investment’; negatively related to leaf lifespan;

represents the carbon economics of leaves

Westoby et al. (2013); Onoda and Wright (2018)

A_n,area Net CO₂ assimilation

rate per area μmol m⁻² s⁻¹ Light- saturated photosynthesis, associated to the light interception of leaves (flux per unit area);

determined by anatomical features and N investment

Westoby et al. (2013); Onoda et al. (2017); Flexas et al. (2021)

Structure

LMA Leaf mass per area g m⁻² Construction cost per unit of light- intercepting leaf area; related to plant growth rate

Poorter et al. (2009); Onoda and Wright (2018)

LT Leaf thickness mm Component of LMA representing the investment

on cell layers on the z axis for light interception

Niinemets (2001); Poorter et al. (2009)

LD Leaf density g cm⁻³ Component of LMA representing the air and

different tissue fractions; related to leaf robustness and water stress tolerance

Niinemets (2001); Poorter et al. (2009)

Pressure– volume traits

LSWC Leaf saturated water content

g g⁻¹ Total amount of water at full saturation per dry mass (inverse of leaf dry matter content, LDMC); related to LD and stress response

Vile et al. (2005); Hodgson et al. (2011)

π_tlp Osmotic potential at the turgor loss point

MPa Potential at the turgor loss or wilting point;

drought tolerance indicator; more negative values related to drier habitats

Bartlett, Scoffoni, et al. (2012);

Zhu et al. (2018) π_o Osmotic potential at

full turgor

MPa Solute concentration in cells at full saturation;

strongly related to π_tlp; decreases under drought (osmotic adjustment)

Bartlett, Scoffoni, et al. (2012)

ε Bulk modulus of

elasticity

MPa Change in pressure potential per change in symplastic water content; higher values (less elastic leaves) related to sclerophylly

Bartlett, Scoffoni, et al. (2012)

f_apo Fraction of apoplast water

unitless Fraction of extracellular water content (non- symplastic water; in cell walls, xylem lumen)

Bartlett, Scoffoni, et al. (2012)

N_os/DM number of osmoles per dry mass

mmol g⁻¹ Number of active osmoles (solutes) contributing to π_o normalised by leaf dry mass

Tyree and Jarvis (1982)

C_ft Tissue- specific capacitance at full turgor

MPa⁻¹ Change in relative water content per water potential change above the turgor loss point, or fraction of water mobilised under application of a pressure of 1 MPa; ‘capacitor’

Scoffoni et al. (2014); Xiong and Nadal (2020)

C*_{ft, mass} Leaf- specific capacitance at full turgor per dry mass

mmol g⁻¹ MPa⁻¹ C_ft normalised by leaf dry mass; absolute amount of water mobilised under 1 MPa; ‘bulk’ leaf capacitance

Blackman and

Brodribb (2011); Scoffoni et al. (2014)

C*_{ft, area} Leaf- specific capacitance at full turgor per area

mol m⁻² MPa⁻¹ C_ft normalised by leaf area; related to leaf hydraulic conductance and stomatal response

Sack et al. (2003); Blackman and Brodribb (2011); Xiong and Nadal (2020)

RWC_tlp Relative water content at the turgor loss point

% RWC (volume) at the turgor loss or wilting point;

partially determined by ε Bartlett, Scoffoni, et al. (2012)

under different conditions (e.g. mature field plants ver- sus potted plants grown outdoors or in a growth cham- ber) and/or displaying young and old leaves (in the case of some evergreens) were included for the same species.

The complete dataset comprehends leaf photosynthesis, bulk structure, and PV measurements for a total of 45 data entries from 37 species. In addition, only for the 20 species included in the study of Nadal et al. (2018) (except Nephrolepis exaltata), we measured leaf anatomy, N and C content, and cell wall composition (see Methods S2 for technical details regarding these measurements).

Gas exchange

Measurements of plants from the UIB campus were conducted in June– July 2020. Mature individuals with no signs of stress were selected for the measurements.

Branches were collected from the field at sunset, re- cut under water to avoid hydraulic failure, and measured the next day in a growth chamber (25– 30°C, 40%– 60%

RH, 300– 600 μmol m⁻² s⁻¹ light intensity). Gas exchange was measured in four to six leaves from different indi- viduals per species and conditions using an open gas exchange system with a coupled 2 or 6 cm² (depending on the species) fluorescence chamber (Li- 6800; Li- Cor Inc.). Light- saturated net CO₂ assimilation per area (A_n,area) was recorded after reaching steady- state at am- bient CO₂ (400 μmol mol⁻¹). All measurements were per- formed at 25°C block temperature, 1– 2  kPa leaf VPD and >1200 μmol m⁻² s⁻¹ PAR irradiance.

Pressure– volume curves

Parameters describing leaf water relations were ob- tained from PV curves conducted in leaves from the same branches collected for gas exchange. Individual leaves or branches (depending on if they were potted or field- sown plants) were rehydrated overnight with distilled water (using the ‘standing’ method of rehydration through the petiole in the case of individual leaves; Arndt et al., 2015).

Measurements were performed in four to six leaves per species and conditions using the bench- dry method, where leaf mass and water potential (Ψleaf; pressure chamber model 600D; PMS Instrument Company) are measured alternately in a dehydrating leaf. Following the protocol and calculations described by Sack and Pasquet- Kok (2011), we obtained the osmotic potential and rela- tive water content at turgor loss point (π_tlp and RWC_tlp respectively), the osmotic potential at full turgor (π_o), the bulk modulus of elasticity calculated from the total water content (ε*), the leaf saturated water content calculated from leaf weight when Ψleaf = 0 MPa (LSWC), the capaci- tance at full turgor (C_ft), and the leaf area- specific capaci- tance at full turgor (C*_ft,area). Additionally, we calculated the fraction of apoplastic water (f_apo) as 1 − x- intercept in

the −1/Ψleaf versus 100 − RWC relationship, and the bulk modulus of elasticity from the symplast water content (ε) as ε =  ε* (1 − f_apo) (Bartlett, Scoffoni, et al., 2012). The number of osmoles (N_o) was calculated as described from equation 3 following Tyree and Jarvis  (1982). Any ‘pla- teau’ effect was discarded from the data points included in the calculations.

Leaf bulk structure

Leaf mass per area and its components were meas- ured in leaves used for either gas exchange or for PV curves across the species included in the study fol- lowing standardised protocols described by Pérez- Harguindeguy et al.  (2013). Leaf thickness (LT) was estimated as the average of six measurements using a digital calliper (three measurements in Olea europaea and Marsilea quadrifolia due to their narrow and small leaves). Leaf area was analysed using the ImageJ soft- ware (Wayne Rasband/NIH). Leaf dry matter (DM) was obtained after >72 h at 70°C. Leaf density (LD) was then calculated as LD = LMA/LT. For most spe- cies included in the study, we also measured the satu- rated weight (SW) after rehydrating the leaves for ~24 h with distilled water in plastic bags at 4°C, thus allow- ing the calculation of ‘measured’ leaf saturated water content as LSWC = (SW − DM)/DM. Mean LMA was used for converting area- to mass- based photosynthe- sis and capacitance (A_n,mass and C*_ft,mass respectively).

Statistical analysis

Unless indicated otherwise, log₁₀- transformation was applied to means (n =  4– 6) per species and conditions for all statistical analyses. Data for the same species under different conditions were considered as independ- ent data points, as in Sack et al. (2003). Analyses were performed using the R statistical software (R version 4.2.0; R Core Team, 2022). Relationships among pa- rameters were quantified using standardised major axis slopes (SMA) and Pearson correlations. The sma func- tion from the ‘smatr’ package (Warton et al., 2012) was used for SMA fittings and rcorr from the ‘Hmisc’ pack- age (Harrell Jr, 2022) for correlations. To discern di- rect versus indirect relationships, path model analyses were conducted through structural equation modelling (SEM) using the sem function from the ‘lavaan’ pack- age (Rossel, 2012) on standardised data means using the scale function (R Core Team, 2022). A priori models were constructed following the theoretical framework displayed in Box 1. Principal component analysis (PCA) was conducted on standardised data using the function prcomp in the ‘stats’ package (R Core Team, 2022); prin- cipal components and their loadings were plotted using the ‘factoextra’ package (Kassambara & Mundt, 2020).

BOX 1 Theory relating LMA components to photosynthesis and PV traits

Net CO₂ assimilation on a mass basis (A_n,mass) can be formulated in terms of assimilation expressed on an area basis (A_n,area) and leaf mass per area (LMA), the latter resulting from a combination of leaf thickness (LT) and density (LD); hence:

LD is defined as dry matter (DM) per leaf volume (LV) at full turgor (Witkowski & Lamont, 1991). LV can be considered as the sum of the saturated water (V_sw), dry matter (V_dm) and air (V_air) volumetric fractions of the leaf (see similar approaches in Roderick, Berry, Noble, et al., 1999; Shipley et al., 2006). When the specific density of the non- gaseous fractions is included, then LD can be directly related to leaf saturated water content (LSWC; g H₂O g⁻¹ DM) as:

being SW the saturated water mass, ρ_w the specific density of water (1 g cm⁻³), and ρ_dm the specific density of the dry matter. The final term a corresponds to 1/ρ_dm+ V_air/DM. Tyree and Jarvis (1982) defined the leaf osmotic po- tential at full turgor (π_o) for non- ideal solutions as the number of osmoles (N_os) per water mass in the symplast at saturation (SW_sym). If N_os is defined in terms of leaf dry matter (N_os/DM; Kwon & Pallardy, 1989), then LSWC can be included in the formula:

where R is the gas constant (8.134 m³ Pa K⁻¹ mol⁻¹), T is the temperature (25°C) and f_apo is the fraction of water in the apoplast. Equation (3) shows a possible relationship between one of the components of LMA (LD through LSWC) with one of the key PV traits, since π_o is the main determinant of water (or osmotic) potential at the tur- gor loss point (π_tlp; Bartlett, Scoffoni, et al., 2012). The other PV parameter that can be linked to the components of LMA is leaf- specific capacitance at full turgor (C*_ft), since water storage (defined by LSWC) is one of the components of C*_ft (Sack & Tyree, 2005; Scoffoni et al., 2014). Indeed, LSWC is directly related to leaf- specific capacitance at full turgor when expressed in absolute terms and normalised by dry mass (C*_ft,mass), equivalent to the ‘bulk’ capacitance defined by Blackman and Brodribb (2011):

where C_ft is the tissue- specific leaf capacitance at full turgor, that is, the fraction of water storage above the turgor loss point (Bartlett, Scoffoni, et al., 2012; Scoffoni et al., 2014). C*_ft,mass is expressed in mmol of H₂O when account- ing for the molar mass of water (Blackman & Brodribb, 2011). Note that C*_ft,area is C*_ft,mass× LMA. According to the formulation of Bartlett, Scoffoni, et al. (2012), C_ft depends on f_apo, π_o and the bulk modulus of elasticity derived from the symplast water content (ε), since these are the parameters describing RWC_tlp and π_tlp:

Equations (1), (3) and (4) point towards a potential central role of LD and LSWC in linking photosynthesis with π_o and capacitance. In turn, LD and LSWC are inversely proportional (Equation 2), although their exact relationship may depend on the nature of the leaf dry matter (ρ_dm) and leaf air spaces. Equation (2) differs from previous similar attempts of linking leaf structure with leaf water content (Roderick, Berry, Noble, et al., 1999; Shipley et al., 2006; Vile et al., 2005) in that the leaf dry and water fractions are separated, thus allowing the connection between LMA components and PV- derived parameters.

A_n,mass= A_n,area (1)

LMA = A_n,area LT × LD

(2) LD = DM

LV = DM

V_sw+ V_dm+ V_air = DM

SW ∕ ρ_w+ DM ∕ ρ_dm+ V_air = 1 LSWC + a

(3) π_o= − R × T × ρ_w× N_os

SW_sym = − R × T × ρ_w× N_os∕ DM

SW_sym∕ DM = − R × T × ρ_w× N_os∕ DM LSWC ×(1 − f_apo)

C^∗_ft,mass=ΔRWC (4) ΔΨ × SW

DM = C_ft× LSWC

C_ft= 1 − RWC_tlp∕ 100 (5)

− π_tlp = (1 − f_apo) × (π_o+ ε) ε²

For the main parameters included in the theoretical framework (Box 1), the relative contribution of the vari- ables determining them was calculated following the vari- ance partitioning approach of Onoda et al. (2011), given that A_n,mass, π_o, C_ft and C*_ft,mass are defined in an exact manner (no error term) from their respective components (Equations 1, 3– 5). For these parameters, the variance of parameter Y equals the sum of its covariance with each of its X_i components: Var(Y) = Cov(Y,X₁) + Cov(Y,X₂) + ⋯ + Cov(Y,X_i). The relative contribution of X_i to the varia- tion of Y can thus be calculated as |Cov(Y,X_i)|/Var(Y). For performing this analysis, eqns. shown in Box 1 need to display the structure Y = X₁+ X₂+ ⋯ + Xi; thus, Equations (1), (3)– (5) where log- transformed (see the resulting equa- tions in Methods S1). We used calculated C_ft and C*_ft,mass using Equations (4) and (5) (Bartlett, Scoffoni, et al., 2012) for this analysis instead of the measured values from PV curves (Sack & Pasquet- Kok, 2011) for producing exact re- lationships for the equations describing C_ft and C*_ft,mass. Nonetheless, the resulting contributions for these param- eters are reliable since the calculated values were almost identical to the measured ones (r > 0.99 for both).

R E SU LTS

Across the included species, A_n,area varied 6- fold (from 5.5 in Nephrolepis exaltata to 32.8  μmol m⁻²  s⁻¹ in Helianthus annuus) and LMA varied 15- fold (from 13.2 in Phaseolus vulgaris to 205 g m⁻² in Arbutus unedo).

This range in the two parameters resulted in a 23- fold variation in A_n,mass (36– 839 nmol g⁻¹  s⁻¹). LMA was re- lated to both LT and LD, being the latter the stronger driver of LMA (R² = 0.53; Figure S1). LT and LD dis- played a similar range of variation (8- and 11- fold respec- tively), although they varied independently of each other (p =  0.10; Figure S1). Notably, the resurrection plant Craterostigma plantagineum presented significantly higher LT (0.87 mm) compared to the rest of species in- cluded in the study (Figure S1). LT values obtained using the digital calliper were strongly correlated with the thickness from anatomical sample analysis using light microscopy (r² = 0.88 for linear fit; Figure S2), support- ing the reliability of our LD estimation. As predicted by theory (Equation 2), LD and LSWC showed a strong inverse relationship across species (Figure 1). Regarding PV traits, π_tlp varied 4- fold (from −0.64 in Solanum lyco- persicum to −2.67 MPa in Quercus ilex) and was mainly determined by π_o (R² = 0.93; p < 0.001), and only weakly related to ε (R² = 0.34; p < 0.001). On the other hand, ε was the main parameter from Equation  (5) driving changes in C_ft (R² = 0.30; p < 0.001; Table S2). C_ft is the parameter related the most to RWC_tlp (Table S2) and is a strong driver of both C*_ft,area and C*_ft,mass (Table S2), which presented 20- and 54- fold variation respectively.

For C*_ft,mass, values ranged from 1.4 in Ficus nitida to 76.3 mmol g⁻¹ MPa⁻¹ in C. plantagineum.

The relationships between leaf structure and pho- tosynthesis and PV traits were considered on the same basis of expression, that is, parameters were compared on either an area or a mass basis. Following this ratio- nale and the theoretical framework presented in Box 1, A_n,area and C*_ft,area were compared to LMA and A_n,mass and C*_ft,mass to LSWC (Figure 2). LMA was not related to area- based A_n and leaf specific capacitance, and only weakly related to π_tlp (Figure 2a– c). On the other hand, mass- based A_n and specific capacitance increased with higher LSWC (Figure 2d,e). π_tlp showed a stronger re- lationship with LSWC than with LMA (R² =  0.45 and 0.18, respectively); and leaves with higher water content presented higher (less negative) π_o and π_tlp (Figure 2f;

Table  S2). Unsurprisingly, this common coordina- tion with LSWC resulted in the scaling of A_n,mass with C*_ft,mass (Figure 3a) and its negative relationship with π_tlp (Figure 3b). Similarly, π_tlp was lower (more negative) in denser leaves (Figure 3c), although the relationship of π_tlp with LD was weaker than that with LSWC (R² = 0.32 and 0.45, respectively). The nature of the coordination between A_n,mass and C*_ft,mass was explored using path analysis, which showed that leaf capacitance had little direct effect on A_n, and that LSWC was directly driving both A_n,mass and C*_ft,mass (Figure S3a). The coordination between A_n,mass and π_tlp followed the same pattern, being LSWC the direct determinant of both (Figure S3b).

F I G U R E 1 Relationship between leaf saturated water content (LSWC) and density (LD). Significant SMA fit (R² = 0.822, p < 0.001) denoted by red line. Circles represent mean for each species and condition. LSWC values correspond to the obtained from PV curves.

The trait framework displayed in Figure 4a sum- marises the main relationships found among photosyn- thesis, leaf structure and PV traits. The connections established between parameters are based on the theory that underpins them (Box 1) and the empirical relation- ships described above. LSWC contributes to ~50% of the explained variance for both A_n,mass (through its effects on LD and LMA) and C*_ft,mass, and to ~40% for π_o. The remaining ~50% of the variance of A_n,mass and C*_ft,mass is explained mainly by A_n,area and ε (the main driver of C_ft). A PCA was conducted (Figure 4b,c) to discern if there is a common axis of variation among the studied leaf traits. Indeed, C*_ft,mass, LSWC, A_n,mass, LD, LMA and π_tlp were strongly associated with PC1 (47% variance explained; Figure 4b, Figure S4), which also partially in- cluded π_o. PC2 (23% variance) was mainly driven by N_os/ DM, f_apo and ε, with minor contribution of π_o (Figure 4b, Figure S4). Notably, A_n,area and LT (and, to a lesser ex- tent, C_ft) were the most independent variables (i.e. the ones not related with neither PC1 nor PC2) among the set of variables in this study (Figure S4). Growth forms were

differentiated along PC1, since the forbs included in the study presented the higher C*_ft,mass, LSWC and A_n,mass compared to the rest of species, whereas the woody ev- ergreen species occupied the other extreme of the axis (Figure 4c).

From the trait framework analysis and PCA, LSWC was identified as the key variable driving most trait variation. Thus, we explored its possible anatomical and chemical determinants in the Nadal et al.  (2018) subset. Among the analysed parameters, LSWC was only related to cell dimensions and to carbon and nitrogen content (Table  S3). LSWC escalated with cross- section cell area (a proxy to cell size), primar- ily of mesophyll (Figure S5a) but also of epidermis cells (Figure S5b); on the other hand, LSWC showed a strong inverse relationship with C per dry mass (Figure S5c) and a weaker, positive relationship with N per dry mass (Figure S5d). No relationship was found for cell wall composition (fraction of celluloses, hemi- celluloses and pectins) with LSWC (Table S3). Notably, mesophyll cell wall thickness was not related to either

F I G U R E 2 Coordination among area- and mass- based photosynthesis, structural and PV traits. Leaf mass per area (LMA) and area- based net CO₂ assimilation (A_{n, area}; a), no significant SMA fit (R² = 0.024, P = 0.311). LMA and leaf area- specific capacitance at full turgor (C*_ft,area; b), no significant SMA fit (R² = 0.009, p = 0.524). LMA and osmotic potential at turgor loss point (π_tlp; c), significant SMA fit (R² = 0.175, p = 0.004). Leaf saturated water content (LSWC) and mass- based net CO₂ assimilation (A_n,mass; d), significant SMA fit (R² = 0.701, p < 0.001).

LSWC and leaf mass- specific capacitance at full turgor (C*_ft,mass; e), significant SMA fit (R² = 0.711, p < 0.001). LSWC and π_tlp (d), significant SMA fit (R² = 0.446, p < 0.001). Circles represent mean for each species and condition. LSWC values correspond to the obtained from PV curves.

A_n,area or ε (Table S3), whereas ε showed a positive rela- tionship with the N:C ratio (Table S3).

DISCUS SION

The present study expands on the previous work presented in Nadal et al. (2018) and Xiong and Nadal (2020), and es- tablishes a central role of the water content per dry mat- ter in determining the link between carbon economics and water relations in leaves. Our dataset covers a significant portion of trait variation; the ranges of LMA, LT and LD are approximately within the fifth and 95th percentiles of their respective range of values as compiled by Hodgson et al. (2011) for ~2000 species. Similarly, data for A_n and ε cover the range of the values reported for the studied groups (Bartlett, Scoffoni, et al., 2012; Flexas & Carriquí, 2020);

in the case of π_tlp, our results fall within the values de- scribed by Bartlett, Scoffoni, et al.  (2012) for non- desert species. The studied species include ferns and resurrection (desiccation- tolerant) plants in addition to woody and her- baceous angiosperms, so we considered disparate growth forms and strategies. Hence, we are confident regarding the representativeness of the studied leaf traits.

Our results show how A_n,mass, C*_ft,mass and π_tlp, shared a common axis of variation (Figures 3a,b and 4a,b), which is driven by the close relationship between LD and LSWC (Figure 1) and their association with A_n,mass and LMA, and C*_ft,mass and π_tlp respectively (Box 1, Figure 4a). The strong correlation between LD and LSWC was predicted by the theory (Equation 2) and supported by numerous reports relating leaf vol- ume to water content (Castro- Díez et al., 2000; Hodgson et al., 2011; Niinemets, 1999; Pyankov et al., 1999; Shipley

& Vu, 2002; Vile et al., 2005). The air volume fraction

accounts for a significant portion of the unexplained variance (~25%) between the two traits. Nonetheless, the air fraction is unlikely to affect water relations; accord- ingly, LSWC is more strongly related to other PV param- eters than LD is (Table S2), thus supporting the overall framework displayed in Figure 4a. This also explains the higher R² of π_tlp with LSWC compared to LMA (0.45 and 0.18 respectively; Figure 2c,f), since the later includes leaf thickness and the air fraction (Box 1). Such pattern is found in other studies considering either LD or water content (Bartlett, Scoffoni, Ardy, et al., 2012;

Fu et al., 2012; Ishida et al., 2008; Liu & Osborne, 2015;

Méndez- Alonzo et al., 2019), partially due to the lack of correlation between π_tlp and LT (Table S2). The associa- tion of π_tlp with photosynthesis and structure was weaker than for the other variables (Figures 2 and 3), since a sig- nificant portion of the variation in π_o (and thus π_tlp) was captured by f_apo and N_o/DM, which were mostly indepen- dent from other traits (Figure 4, Figure S4). Nonetheless, our approach suggests that the association of π_tlp and LMA proposed by Zhu et al. (2018) results from LSWC rather than LMA directly affecting π_tlp.

A key relationship emerging among the set of leaf traits is the positive scaling between A_n,mass and C*_ft,mass (Figure 3a). This relationship might be expected since both photosynthesis and capacitance are positively re- lated to leaf hydraulics, namely K_leaf, due to the need for higher water transport and buffering capacity in leaves with high photosynthetic capacity— and thus elevated transpiration (Brodribb et al., 2007; Sack et al., 2003;

Sack & Tyree, 2005; Xiong & Nadal, 2020). However, the path analysis (Figure S3a) shows that rather than one di- rectly driving the other, this is an indirect relationship through the effect of LSWC on both A_n,mass and C*_ft,mass. LSWC is associated with ‘bulk’ rather than ‘dynamic’

F I G U R E 3 Relationship between leaf photosynthesis, density and PV traits. Net CO₂ assimilation on a mass basis (A_n,mass) and leaf mass- specific capacitance at full turgor (C*_ft,mass; a), significant SMA fit (R² = 0.625, p < 0.001). A_n,mass and osmotic potential at turgor loss point (π_tlp; b), significant SMA fit (R² = 0.303, p < 0.001). Leaf density (LD) and π_tlp (b), significant SMA fit (R² = 0.317, p < 0.001). Circles represent mean for each species and condition.

capacitance, being the latter involved in the water trans- port inside leaves (Blackman & Brodribb, 2011); sepa- rating C*_ft,mass (which reflects bulk capacitance) into its components may help in discerning the degree of hydrau- lic compartmentalisation involved in the relationship be- tween A_n,mass and LSWC (Xiong & Nadal, 2020). On the other hand, few studies have explored the intrinsic mech- anisms that drive the relationship between A_n and LSWC under non- stressed conditions. The scaling of photosyn- thesis with increasing water content (Figure 2d) may be due to photosynthesis rate per volume being relatively

conserved across species; indeed, some studies have argued for such expression— photosynthesis rate per volume or water content— given that living systems op- erate on the basis of product concentration (Garnier et al., 1999; Roderick, Berry, Noble, et al., 1999). A recent meta- analysis by Wang et al. (2022) also shows a positive correlation between photosynthesis and leaf water con- tent across a wide range of ecosystems and plant func- tional types, which is attributed to a positive relationship between metabolic processes (i.e. photosynthesis) and water content (Huang et al., 2020).

F I G U R E 4 Multivariate analysis of leaf photosynthesis, structure and PV traits. A conceptual trait framework can be derived from the trait relationships described in Box 1 (a); the relative contributions from the partitioning of variance analysis are displayed as arrows and solid lines connecting the different parameters. These parameters are net CO₂ assimilation on a mass basis (A_n,mass), derived from area- based assimilation (A_n,area) and leaf mass per area (LMA), the latter defined by leaf thickness (LT) and density (LD); osmotic potential at full turgor (π_o), derived from leaf saturated water content (LSWC), the number of osmoles per dry matter (N_os/DM) and the fraction of apoplastic water ( f_apo); capacitance at full turgor (C_ft), derived from π_o, f_apo and the bulk modulus of elasticity from symplast water content (ε); and the leaf mass- specific capacitance at full turgor (C*_ft,mass), derived from C_ft and LSWC. The equations for calculating the relative contributions are described in Methods S1. The ‘*’ denotes split variance from the π_o+ ε component in Equation (5) (Box 1). LSWC values correspond to the obtained from PV curves. Dashed lines and numbers (R²) in italics refer to SMA fittings between the indicated variables, where no exact partition of variance is possible. The osmotic potential at turgor loss point (π_tlp) is only related to π_o since osmotic potential is its main driver (Bartlett, Scoffoni, et al., 2012). Positive and negative relationships are represented by red and blue lines/arrows, respectively. The contribution of each parameter to the two principal components (PC1 and PC2) from the PCA results is indicated by the colour of the boxes. Vectors (b) indicate variable loadings and circles (c) represent means for each species and condition. Growth forms that include ≥5 species are depicted by concentration ellipses (level set at 0.8 in the fviz_pca_ind function).

The central role of LSWC supports the framework pro- posed by Shipley et al. (2006), which pointed towards a key role of water content— more precisely, cell volume per cell wall— in driving the coordination between A_n,mass, N_mass and LMA. Measuring cell and cell wall volumes is rather difficult; instead, we show a positive correlation between LSWC and cross- sectional cell area (Figure S5a,b), indi- cating that a high water content reflects larger cells (and hence increased cell volume per cell wall). This result is in accordance with previous studies relating cell dimen- sions to leaf density (Castro- Díez et al., 2000; Garnier &

Laurent, 1994; Pyankov et al., 1999; Villar et al., 2013).

Additionally, in larger cells, the proportion of cell wall in the total dry matter would be lower due to their in- creased volume per cell wall, thus explaining the lower C_mass and higher N_mass and N:C with increasing LSWC (Figure S5c,d, Table S3), as reported in Roderick, Berry, Saunders, et al. (1999). Hence, our findings support the central role of cell dimensions in explaining the trade- offs among the LES traits (Shipley et al., 2006). Nonetheless, it is worth noting that cell walls present an additional ef- fect on area- based traits (A_n,area, LMA), given the greater presence of thicker cell walls in high LMA species and their effect on mesophyll conductance (g_m), thus poten- tially limiting CO₂ diffusion in leaves with reduced chlo- roplast exposition (Flexas et al., 2021; Nadal et al., 2021;

Onoda et al., 2017; Peguero- Pina, Sisó, et al., 2017).

In Nadal et al.  (2018), we suggested that cell wall thickness (reflected by g_m and diffusion limitations) could constitute a common basis for the relationship between elasticity and photosynthesis; however, here we show that the relationship between ε and A_n,area is not at the basis of the proposed coordination (in fact A_n,area and ε vary independently in this extended dataset;

Figure S4a, Table S2), and that ε is contributing directly only to capacitance (Figure 4a). Neither ε nor A_n,area were related to mesophyll cell wall thickness (Table S3), despite theory relating wall thickness with the mechan- ical properties of single cells (Tyree & Jarvis, 1982), and some previous data in oaks (Peguero- Pina, Sancho- Knapik, et al., 2017). The lack of relationship between A_n,area and cell wall thickness may be expected in rela- tively small datasets with limited thickness range (here, 0.1– 0.6 μm; see Flexas et al., 2021). The fact that N:C was related to ε (Table S3), where leaves with a higher N pro- portion are more elastic, indicates a potential subsidiary association between photosynthesis and ε, given the N requirements of photosynthesis and the trade- off in N allocation between the cell walls and the photosynthetic apparatus (Onoda et al., 2017). Few studies have consid- ered N content as a driver of leaf elasticity (e.g. Onoda et al., 2008); further exploration of the role of N- rich components in cell walls is required to discern the basis of ε. Nonetheless, ε is independent of the other variables captured by the PC1 (Figure 4b, Figure S4), and so any potential association with A_n would come second to that of photosynthesis with LSWC and capacitance.

Overall, the theory and results presented here provide a basis for the coordination between leaf photosynthesis and structure with water relations derived from PV traits, with the saturated water content of leaves playing a central role: on the one hand, LSWC determines the coordination between A_n,mass, C*_ft,mass and π_tlp, and, on the other hand, it constitutes a direct link to LMA through LD. Such central role of LSWC has been recently acknowledged by Wang et al. (2022), where the coordination of A_n,mass and leaf water content is extended to the global level. In our dataset, A_n,mass and not A_n,area displayed the most signifi- cant associations with the other leaf traits (Figures 2 and 4, Figure S4). Hence, the relationships established here are more in line with an ‘investment and return’ perspective of leaf function, which is provided by considering traits on a mass basis (Westoby et al., 2013). In effect, not only A_n,mass (contrary to A_n,area) is tightly coordinated with leaf lifespan (Onoda & Wright, 2018; Wright et al., 2004);

water content is also related to leaf lifespan (Garnier &

Navas, 2012; Ishida et al., 2008; Ryser, 1996; Ryser &

Urbas, 2000). Given than PV traits are usually thought in terms of drought tolerance (Bartlett, Scoffoni, et al., 2012;

Niinemets, 2001), their closer association with other traits directly related to the economy of leaves can be expected.

Notably, our approach confers a relatively diminished role to LMA, given that A_n,mass can be directly related to LSWC, and emphasises the water content in leaves as the main explanatory variable. Note that LSWC is the inverse of leaf dry matter content (LDMC) or the ratio of dry matter per saturated weight (Pérez- Harguindeguy et al., 2013), since LDMC = 1/(LSWC + 1). This is a param- eter often highlighted as a better predictor of leaf perfor- mance in response to various biotic and abiotic factors (Hodgson et al., 2011; Kazakou et al., 2009; Laughlin et al., 2010; Pontes et al., 2007; Quested et al., 2007; Roche et al., 2004; Rusch et al., 2009; Schädler et al., 2003;

Smart et al., 2017; Wang et al., 2022; Wilson et al., 1999;

Witkowski & Lamont, 1991), and even more reliable than LMA when intending to capture spatiotemporal varia- tions among species (Garnier et al., 2001). However, LMA is still predominant in the literature, partially due to ‘his- torical inertia’ (Hodgson et al., 2011); on the other hand, we argue that LSWC (or LDMC) reflects the link between leaf structure and function more accurately than LMA, given its stronger relationship with photosynthesis and PV traits. Since LSWC measured from rehydrated leaves compares remarkably well to that obtained from PV curves (Figure S6) and this is a relatively easy- to- measure parameter (Pérez- Harguindeguy et al.,  2013; Smart et al., 2017; Vaieretti et al., 2007), we strongly recommend using LSWC in future ecophysiological studies aiming to explore leaf and plant functional traits.

AU T HOR CON T R I BU T IONS

Miquel Nadal, Javier Gulías and Jaume Flexas planned the research; Miquel Nadal, María J. Clemente- Moreno, Margalida Roig- Oliver and Alicia V. Perera- Castro

performed the measurements; Miquel Nadal, Jaume Flexas and Yusuke Onoda developed the theoretical framework; Miquel Nadal analysed the data and drafted the manuscript. All authors contributed to subsequent revisions and discussion.

AC K NO W LE DGE M EN TS

This work was supported by the project PGC2018- 093824- B- C41 from the Ministerio de Ciencia, Innovación y Universidades (Spain) and the European Regional Development Fund (ERDF). MN was supported pre- doctoral fellowship BES- 2015- 072578, financed by the Ministerio de Economía y Competitividad (MINECO) and the European Social Fund; and postdoctoral fellow- ship Juan de la Cierva- Formación (FJC2020- 043902- I), fi- nanced by MCIN/AEI/10.13039/501100011033 (Spain) and the European Union (‘NextGenerationEU/PRTR’). AVP- C was supported by the Ministerio de Educación, Cultura y Deporte (pre- doctoral fellowship FPU- 02054). MR- O was supported by the MINECO, pre- doctoral fellowship FPU16/01544. YO was supported by grant JSPS KAKENHI

#21H02564 (Japan). We thank the technical support for microscopy preparation provided by the Universitat de València (Secció de Microscopia Electrònica, SCSIE), Universidad de Murcia (Servicio de Apoyo a las Ciencias Experimentales) and Dr. Ferran Hierro (UIB, Serveis Cientificotècnics), and CEBAS- CSIC (Murcia, Spain) Ionomic Service for C and N quantification. We thank Dr. Eva Miedes (CBGP, UPM- INIA) for her help in cell wall composition analysis. We also thank Miquel Truyols and collaborators of the UIB Experimental Field and Glasshouses which are supported by the UIB Grant15/2015.

F U N DI NG I N FOR M AT ION

European Regional Development Fund, Grant/Award Number: PGC2018- 093824- B- C41; European Social Fund, Grant/Award Number: BES- 2015- 072578; Japan Society for the Promotion of Science, Grant/Award Number: KAKENHI #21H02564; Ministerio de Ciencia e Innovación, Grant/Award Number: FJC2020- 043902- I;

Ministerio de Ciencia, Innovación y Universidades, Grant/

Award Number: PGC2018- 093824- B- C41; Ministerio de Economía y Competitividad, Grant/Award Number:

BES- 2015- 072578FPU16/01544; Ministerio de Educación, Cultura y Deporte, Grant/Award Number: FPU- 02054 CON F LICT OF I N T ER E ST STAT EM EN T None.

PEER R EV I EW

The peer review history for this article is available at https://publo ns.com/publo n/10.1111/ele.14176.

DATA AVA I LA BI LI T Y STAT EM EN T

Data supporting the manuscript are available in the Dryad Digital Repository: https://doi.org/10.5061/dryad.

mgqnk 993k.

ORC I D

Miquel Nadal  https://orcid.org/0000-0003-1472-1792 R E F E R E NC E S

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